SoroushVahidi/scidocs huggingface dataset:
COMPUTATION OF CONDITIONAL PROBABILITY STATISTICS BY 8-MONTH-OLD INFANTS
(1998)

321 Abstract— A recent report demonstrated that 8-month-olds can segment a continuous stream of speech syllables, containing no acoustic or prosodic cues to word boundaries, into wordlike units after only 2 min of listening experience (Saffran, Aslin, & Newport, 1996). Thus, a powerful learning mechanism capable of extracting statistical information from fluent speech is available early in development. The present study extends these results by documenting the particular type of statistical computation—transitional (conditional) probability—used by infants to solve this word-segmentation task. An artificial language corpus, consisting of a continuous stream of trisyllabic nonsense words, was presented to 8-month-olds for 3 min. A postfamiliarization test compared the infants' responses to words versus part-words (tri-syllabic sequences spanning word boundaries). The corpus was constructed so that test words and part-words were matched in frequency, but differed in their transitional probabilities. Infants showed reliable discrimination of words from part-words, thereby demonstrating rapid segmentation of continuous speech into words on the basis of transitional probabilities of syllable pairs. Many aspects of the patterns of human languages are signaled in the speech stream by what is called distributional evidence, that is, regularities in the relative positions and order of elements over a corpus of utterances (Bloomfield, 1933; Maratsos & Chalkley, 1980). This type of evidence, along with linguistic theories about the characteristics of human languages, is what comparative linguists use to discover the structure of exotic languages (Harris, 1951). Similarly, this type of evidence , along with tendencies to perform certain kinds of analyses on language input (Chomsky, 1957), could be used by human language learners to acquire their native languages. However, using such evidence would require rather complex distributional and statistical computations , and surprisingly little is known about the abilities of human infants and young children to perform these computations. By using the term computation, we do not mean, of course, that infants are consciously performing a mathematical calculation, but rather that they might be sensitive to and able to store quantitative aspects of distribu-tional information about a language corpus. Recently, we have begun studying this problem by investigating the abilities of human learners to use statistical information to dis-Words are known to vary dramatically from one language to another, so finding the words of a language is clearly a problem that must involve learning from the linguistic environment. Moreover, the beginnings and ends of the sequences of sounds that form words in a …
A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws
(2016)

As ball screw has complex structure and long range of distribution, single signal collected by one sensor is difficult to express its condition fully and accurately. Multi-sensor data fusion usually has a better effect compared with single signal. Multi-sensor data fusion based on neural network(BP) is a commonly used multi-sensor data fusion method, but its application is limited by local optimum problem. Aiming at this problem, a multi-sensor data fusion method based on deep learning for ball screw is proposed in this paper. Deep learning, which consists of unsupervised learning and supervised learning, is the development and evolution of traditional neural network. It can effectively alleviate the optimization difficulty. Parallel superposition on frequency spectra of signals is directly done in the proposed deep learning-based multi-sensor data fusion method, and deep belief networks(DBN) are established by using fused data to adaptively mine available fault characteristics and automatically identify the degradation condition of ball screw. Test is designed to collect vibration signals of ball screw in 7 different degradation conditions by using 5 acceleration sensors installed on different places. The proposed fusion method is applied in identifying the degradation degree of ball screw in the test to demonstrate its efficacy. Finally, the multi-sensor data fusion based on neural network is also applied in degradation degree monitoring. The monitoring accuracy of deep learning-based multi-sensor data fusion is higher compared with that of neural network-based multi-sensor data fusion, which means the proposed method has more superiority.
Dual methods for nonconvex spectrum optimization of multicarrier systems
(2006)

The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations
A Comprehensive Framework for Evaluation in Design Science Research
(2012)

Evaluation is a central and essential activity in conducting rigorous Design Science Research (DSR), yet there is surprisingly little guidance about designing the DSR evaluation activity beyond suggesting possible methods that could be used for evaluation. This paper extends the notable exception of the existing framework of Pries-Heje et al [11] to address this problem. The paper proposes an extended DSR evaluation framework together with a DSR evaluation design method that can guide DSR researchers in choosing an appropriate strategy for evaluation of the design artifacts and design theories that form the output from DSR. The extended DSR evaluation framework asks the DSR researcher to consider (as input to the choice of the DSR evaluation strategy) contextual factors of goals, conditions, and constraints on the DSR evaluation, e.g. the type and level of desired rigor, the type of artifact, the need to support formative development of the designed artifacts, the properties of the artifact to be evaluated, and the constraints on resources available, such as time, labor, facilities, expertise, and access to research subjects. The framework and method support matching these in the first instance to one or more DSR evaluation strategies, including the choice of ex ante (prior to artifact construction) versus ex post evaluation (after artifact construction) and naturalistic (e.g., field setting) versus artificial evaluation (e.g., laboratory setting). Based on the recommended evaluation strategy(ies), guidance is provided concerning what methodologies might be appropriate within the chosen strategy(ies).
Clustering to Find Exemplar Terms for Keyphrase Extraction
(2009)

Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graphbased ranking methods (TextRank) by 9.5% in F1-measure.
TextRay: Mining Clinical Reports to Gain a Broad Understanding of Chest X-Rays
(2018)

The chest X-ray (CXR) is by far the most commonly performed radiological examination for screening and diagnosis of many cardiac and pulmonary diseases. There is an immense world-wide shortage of physicians capable of providing rapid and accurate interpretation of this study. A radiologist-driven analysis of over two million CXR reports generated an ontology including the 40 most prevalent pathologies on CXR. By manually tagging a relatively small set of sentences, we were able to construct a training set of 959k studies. A deep learning model was trained to predict the findings given the patient frontal and lateral scans. For 12 of the findings we compare the model performance against a team of radiologists and show that in most cases the radiologists agree on average more with the algorithm than with each other.
Fast bayesian compressive sensing using Laplace priors
(2009)

In this paper we model the components of the compressive sensing (CS) problem using the Bayesian framework by utilizing a hierarchical form of the Laplace prior to model sparsity of the unknown signal. This signal prior includes some of the existing models as special cases and achieves a high degree of sparsity. We develop a constructive (greedy) algorithm resulting from this formulation where necessary parameters are estimated solely from the observation and therefore no user-intervention is needed. We provide experimental results with synthetic 1D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.
Structure, culture and Simmelian ties in entrepreneurial firms
(2002)

This article develops a cultural agreement approach to organizational culture that emphasizes how clusters of individuals reinforce potentially idiosyncratic understandings of many aspects of culture including the structure of network relations. Building on recent work concerning Simmelian tied dyads (defined as dyads embedded in three-person cliques), the research examines perceptions concerning advice and friendship relations in three entrepreneurial firms. The results support the idea that Simmelian tied dyads (relative to dyads in general) reach higher agreement concerning who is tied to whom, and who are embedded together in triads in organizations. © 2002 Elsevier Science B.V. All rights reserved.
Thinking like a nurse: a research-based model of clinical judgment in nursing.
(2006)

This article reviews the growing body of research on clinical judgment in nursing and presents an alternative model of clinical judgment based on these studies. Based on a review of nearly 200 studies, five conclusions can be drawn: (1) Clinical judgments are more influenced by what nurses bring to the situation than the objective data about the situation at hand; (2) Sound clinical judgment rests to some degree on knowing the patient and his or her typical pattern of responses, as well as an engagement with the patient and his or her concerns; (3) Clinical judgments are influenced by the context in which the situation occurs and the culture of the nursing care unit; (4) Nurses use a variety of reasoning patterns alone or in combination; and (5) Reflection on practice is often triggered by a breakdown in clinical judgment and is critical for the development of clinical knowledge and improvement in clinical reasoning. A model based on these general conclusions emphasizes the role of nurses' background, the context of the situation, and nurses' relationship with their patients as central to what nurses notice and how they interpret findings, respond, and reflect on their response.
Efficient Sparse Coding in Early Sensory Processing: Lessons from Signal Recovery
(2012)

Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an efficient way. We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. Typical parts represent large-scale correlations, thus they can be significantly compressed. Atypical parts, on the other hand, represent local features and are the subjects of actual sparse coding. When applied on natural images, our decomposition based sparse coding model can efficiently form overcomplete codes and both center-surround and oriented filters are obtained similar to those observed in the retina and the primary visual cortex, respectively. Therefore we hypothesize that the proposed computational architecture can be seen as a coherent functional model of the first stages of sensory coding in early vision.
The Security Development Lifecycle in the Context of Accreditation Policies and Standards
(2017)

The proposed security development lifecycle (SecDLC) model delivers a perpetual cycle of information security management and refinement. Using real-world examples, the authors show how SecDLC ensures the goals of preserving, monitoring, and improving security practices, policies, and standards in private and public sectors. The authors describe the four phases of SecDLC, comparing and contrasting them to existing security development models.
Upgrading Lignocellulosic Biomasses : Hydrogenolysis of Platform Derived Molecules Promoted by Heterogeneous Pd-Fe Catalysts
(2017)

This review provides an overview of heterogeneous bimetallic Pd-Fe catalysts in the C–C and C–O cleavage of platform molecules such as C2–C6 polyols, furfural, phenol derivatives and aromatic ethers that are all easily obtainable from renewable cellulose, hemicellulose and lignin (the major components of lignocellulosic biomasses). The interaction between palladium and iron affords bimetallic Pd-Fe sites (ensemble or alloy) that were found to be very active in several sustainable reactions including hydrogenolysis, catalytic transfer hydrogenolysis (CTH) and aqueous phase reforming (APR) that will be highlighted. This contribution concentrates also on the different synthetic strategies (incipient wetness impregnation, deposition-precipitaion, co-precipitaion) adopted for the preparation of heterogeneous Pd-Fe systems as well as on the main characterization techniques used (XRD, TEM, H2-TPR, XPS and EXAFS) in order to elucidate the key factors that influence the unique catalytic performances observed.
Proposed embedded security framework for Internet of Things (IoT)
(2011)

IoT is going to be an established part of life by extending the communication and networking anytime, anywhere. Security requirements for IoT will certainly underline the importance of properly formulated, implemented, and enforced security policies throughout their life-cycle. This paper gives a detailed survey and analysis of embedded security, especially in the area of IoT. Together with the conventional security solutions, the paper highlights the need to provide in-built security in the device itself to provide a flexible infrastructure for dynamic prevention, detection, diagnosis, isolation, and countermeasures against successful breaches. Based on this survey and analysis, the paper defines the security needs taking into account computational time, energy consumption and memory requirements of the devices. Finally, this paper proposes an embedded security framework as a feature of software/hardware co-design methodology.
A New Modular Bipolar High-Voltage Pulse Generator
(2017)

Adapting power-electronic converters that are used in pulsed-power application attracted considerable attention during recent years. In this paper, a modular bipolar high-voltage pulse generator is proposed based on the voltage multipliers and half-bridge converters concept by using power electronics switches. This circuit is capable to generate repetitive high voltage bipolar pulses with a flexible output pattern, by using low voltage power supply and elements. The proposed topology was simulated in MATLAB/Simulink. To verify the circuit operation a four-stage laboratory prototype has been assembled and tested. The results confirm the theoretical analysis and show the validity of the converter scheme.
JOINT IMAGE SHARPENING AND DENOISING BY 3 D TRANSFORM-DOMAIN COLLABORATIVE FILTERING
(2007)

In order to simultaneously sharpen image details and attenuate noise, we propose to combine the recent blockmatching and 3D Þltering (BM3D) denoising approach, based on 3D transform-domain collaborative Þltering, with alpha-rooting, a transform-domain sharpening technique. The BM3D exploits grouping of similar image blocks into 3D arrays (groups) on which collaborative Þltering (by hard-thresholding) is applied. We propose two approaches of sharpening by alpha-rooting; the Þrst applies alpharooting individually on the 2D transform spectra of each grouped block; the second applies alpha-rooting on the 3D-transform spectra of each 3D array in order to sharpen Þne image details shared by all grouped blocks and further enhance the interblock differences. The conducted experiments with the proposed method show that it can preserve and sharpen Þne image details and effectively attenuate noise.
ACIQ: Analytical Clipping for Integer Quantization of neural networks
(2018)

Unlike traditional approaches that focus on the quantization at the network level, in this work we propose to minimize the quantization effect at the tensor level. We analyze the trade-off between quantization noise and clipping distortion in low precision networks. We identify the statistics of various tensors, and derive exact expressions for the mean-square-error degradation due to clipping. By optimizing these expressions, we show marked improvements over standard quantization schemes that normally avoid clipping. For example, just by choosing the accurate clipping values, more than 40% accuracy improvement is obtained for the quantization of VGG16-BN to 4-bits of precision. Our results have many applications for the quantization of neural networks at both training and inference time. One immediate application is for a rapid deployment of neural networks to low-precision accelerators without time-consuming fine tuning or the availability of the full datasets.
Latent fault detection in large scale services
(2012)

Unexpected machine failures, with their resulting service outages and data loss, pose challenges to datacenter management. Existing failure detection techniques rely on domain knowledge, precious (often unavailable) training data, textual console logs, or intrusive service modifications. We hypothesize that many machine failures are not a result of abrupt changes but rather a result of a long period of degraded performance. This is confirmed in our experiments, in which over 20% of machine failures were preceded by such latent faults. We propose a proactive approach for failure prevention. We present a novel framework for statistical latent fault detection using only ordinary machine counters collected as standard practice. We demonstrate three detection methods within this framework. Derived tests are domain-independent and unsupervised, require neither background information nor tuning, and scale to very large services. We prove strong guarantees on the false positive rates of our tests.
An Optimal Auction Mechanism for Mobile Edge Caching
(2018)

With the explosive growth of wireless data, mobile edge caching has emerged as a promising paradigm to support mobile traffic recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality. Finally, extensive simulations are implemented to evaluate the performance of the proposed optimal auction mechanism, and the impact of various model parameters is highlighted to obtain engineering insights into the content caching problem.
Go-ICP: Solving 3D Registration Efficiently and Globally Optimally
(2013)

Registration is a fundamental task in computer vision. The Iterative Closest Point (ICP) algorithm is one of the widely-used methods for solving the registration problem. Based on local iteration, ICP is however well-known to suffer from local minima. Its performance critically relies on the quality of initialization, and only local optimality is guaranteed. This paper provides the very first globally optimal solution to Euclidean registration of two 3D point sets or two 3D surfaces under the L2 error. Our method is built upon ICP, but combines it with a branch-and-bound (BnB) scheme which searches the 3D motion space SE(3) efficiently. By exploiting the special structure of the underlying geometry, we derive novel upper and lower bounds for the ICP error function. The integration of local ICP and global BnB enables the new method to run efficiently in practice, and its optimality is exactly guaranteed. We also discuss extensions, addressing the issue of outlier robustness.